Parallel Computing Application
•Download as PPTX, PDF•
0 likes•322 views
This research article proposes accelerating a geodesic ray-tracing algorithm for fiber tracking in brain imaging using parallel programming on a GPU. Fiber tracking uses diffusion MRI to noninvasively examine brain fiber structures at a microscopic level. While geodesic ray-tracing is robust, it is computationally expensive to reliably find all fibers between seed points and target regions. The authors implemented a highly parallel version of the algorithm using NVIDIA's CUDA platform on a GPU. This provided a significant reduction in running time of up to 40x compared to a multithreaded CPU implementation, greatly increasing the applicability of the algorithm.
Report
Share
Report
Share
Recommended
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...
Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
YouTube: https://youtu.be/gV-rvV3iFDA
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Presented by: Pierre-Etienne Martin
Zeeshan.ali.presentations
The document discusses medical image segmentation and summarizes four research papers. It introduces the task of medical image segmentation to segment organs or lesions. It describes challenges with digital pathology images like dense areas and variations between and within classes. It summarizes four papers on nuclei segmentation: Hover-Net which uses horizontal and vertical distances to separate overlapping nuclei; MILD-Net which uses minimal information loss and dilated convolutions to segment glands; a method using object-aware embeddings to segment biomedical images; and CIA-Net which aggregates multi-level spatial and texture information between decoders to robustly segment nuclei and contours.
Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...
Paper: http://ceur-ws.org/Vol-2882/paper72.pdf
Sabarinathan D and Suganya Ramamoorthy : Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attention Unit. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Colorectal cancer is the third most common cause of cancer worldwide. In the era of medical Industry, identifying colorectal cancer in its early stages has been a challenging problem. Inspired by these issues, the main objective of this paper is to develop a Multi supervision net algorithm for segmenting polys on a comprehensive dataset. The risk of colorectal cancer could be reduced by early diagnosis of poly during a colonoscopy. The disease and their symptoms are highly varying and always a need for a continuous update of knowledge for the doctors and medical analyst. The diseases fall into different categories and a small variation of symptoms may lead to higher rate of risk. We have taken Medico polyp challenge dataset, which consists of 1000 segmented polyp images from gastrointestinal track. We proposed an efficient Net B4 as a pre-trained architecture in multi-supervision net. The model is trained with multiple output layers. We present quantitative results on colorectal dataset to evaluate the performance and achieved good results in all the performance metrics. The experimental results proved that the proposed model is robust and provides a good level of accuracy in segmenting polyps on a comprehensive dataset for different metrics such as Dice coefficient, Recall, Precision and F2.
An Image Segmentation and Classification for Brain Tumor Detection using Pill...
An Image Segmentation and Classification for Brain Tumor Detection using Pill...Association of Scientists, Developers and Faculties
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.Using Mask R CNN to Isolate PV Panels from Background Object in Images
The document describes using Mask R-CNN, a deep learning algorithm, to identify and isolate solar photovoltaic panels from background objects in aerial images. The algorithm was able to accurately detect PV panels and generate pixel masks to remove background pixels from the images. The model performed well with over 75% average precision and 77% accuracy when trained on a small dataset of images. The ability to isolate PV panels from images can help analyze large solar installations and detect potential faults or issues.
Energy-based Model for Out-of-Distribution Detection in Deep Medical Image Se...
Presented work is accepted in Korean domestic conference, Korean Society of Artificial Intelligence in Medicine (KOSAIM) 2020, as a poster session.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Techniques of Brain Cancer Detection from MRI using Machine Learning
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
Brain Tumor Area Calculation in CT-scan image using Morphological Operations
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Recommended
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...
Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
YouTube: https://youtu.be/gV-rvV3iFDA
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Presented by: Pierre-Etienne Martin
Zeeshan.ali.presentations
The document discusses medical image segmentation and summarizes four research papers. It introduces the task of medical image segmentation to segment organs or lesions. It describes challenges with digital pathology images like dense areas and variations between and within classes. It summarizes four papers on nuclei segmentation: Hover-Net which uses horizontal and vertical distances to separate overlapping nuclei; MILD-Net which uses minimal information loss and dilated convolutions to segment glands; a method using object-aware embeddings to segment biomedical images; and CIA-Net which aggregates multi-level spatial and texture information between decoders to robustly segment nuclei and contours.
Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...
Paper: http://ceur-ws.org/Vol-2882/paper72.pdf
Sabarinathan D and Suganya Ramamoorthy : Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attention Unit. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Colorectal cancer is the third most common cause of cancer worldwide. In the era of medical Industry, identifying colorectal cancer in its early stages has been a challenging problem. Inspired by these issues, the main objective of this paper is to develop a Multi supervision net algorithm for segmenting polys on a comprehensive dataset. The risk of colorectal cancer could be reduced by early diagnosis of poly during a colonoscopy. The disease and their symptoms are highly varying and always a need for a continuous update of knowledge for the doctors and medical analyst. The diseases fall into different categories and a small variation of symptoms may lead to higher rate of risk. We have taken Medico polyp challenge dataset, which consists of 1000 segmented polyp images from gastrointestinal track. We proposed an efficient Net B4 as a pre-trained architecture in multi-supervision net. The model is trained with multiple output layers. We present quantitative results on colorectal dataset to evaluate the performance and achieved good results in all the performance metrics. The experimental results proved that the proposed model is robust and provides a good level of accuracy in segmenting polyps on a comprehensive dataset for different metrics such as Dice coefficient, Recall, Precision and F2.
An Image Segmentation and Classification for Brain Tumor Detection using Pill...
An Image Segmentation and Classification for Brain Tumor Detection using Pill...Association of Scientists, Developers and Faculties
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.Using Mask R CNN to Isolate PV Panels from Background Object in Images
The document describes using Mask R-CNN, a deep learning algorithm, to identify and isolate solar photovoltaic panels from background objects in aerial images. The algorithm was able to accurately detect PV panels and generate pixel masks to remove background pixels from the images. The model performed well with over 75% average precision and 77% accuracy when trained on a small dataset of images. The ability to isolate PV panels from images can help analyze large solar installations and detect potential faults or issues.
Energy-based Model for Out-of-Distribution Detection in Deep Medical Image Se...
Presented work is accepted in Korean domestic conference, Korean Society of Artificial Intelligence in Medicine (KOSAIM) 2020, as a poster session.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Techniques of Brain Cancer Detection from MRI using Machine Learning
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
Brain Tumor Area Calculation in CT-scan image using Morphological Operations
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Survey of using gpu cuda programming model
With the technology development of medical industry, processing data is expanding rapidly and computation time also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse sequences and the growing complexity of algorithms. Graphics processing unit (GPU) addresses these problems and gives the solutions for using their features such as, high computation throughput, high memory bandwidth, support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA) is a popular GPU programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed and the computational gain is discussed. A few open issues, hardware configurations and optimization principles of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.
Ieee brain tumor
This document presents a comparative study of two segmentation methods - k-means clustering and fuzzy c-means clustering with genetic algorithm - for detecting brain tumors in MRI images. K-means clustering is used to segment MRI images into clusters and identify tumor regions. Fuzzy c-means clustering with genetic algorithm aims to improve upon k-means by eliminating over-segmentation issues and providing faster, more efficient clustering results. The experimental results indicate fuzzy c-means performs better than k-means for brain tumor segmentation. The document also reviews several other related works applying techniques like edge detection and probabilistic neural networks to segment brain tumors from MRI scans.
Clustering of medline documents using semi supervised spectral clustering
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
Deep Learning-based Fully Automated Detection and Quantification of Acute Inf...
Presented work is accepted at RSNA 2020, Scientific Section.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
An annotation sparsification strategy for 3D medical image segmentation via r...
Review : An annotation sparsification strategy for 3D medical image segmentation via representative selection and self-training (University of Notre Dame , AAAI 2020)
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
H0114857
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING
Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edge detector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used for subsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combine the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accurate background is identified.
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from XailientEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.Mri brain image segmentatin and classification by modified fcm &svm akorithm
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A ...
The document discusses a proposed unified framework for progressively recovering images from corrupted observations through hybrid graph Laplacian regularization. The framework first constructs a multiscale representation of the target image using a Laplacian pyramid. It then progressively recovers the degraded image from coarse to fine scales, allowing for recovery of sharp edges and texture. Within each scale, a graph Laplacian regularization model is learned that minimizes error while preserving image structure. Between scales, the model is extended to a high-dimensional feature space to describe interscale correlations and propagate local structure regularity from coarser to finer scales. Experimental results on impulse noise removal demonstrate better performance than state-of-the-art algorithms.
Similarity validation based nonlocal means image denoising
This document discusses a method to improve the performance of nonlocal means image denoising. It proposes a pre-processing step that eliminates dissimilar patches from the search neighborhood before denoising. A hard threshold is calculated based on the distribution of distances between noisy similar patches. The proposed Similarity Validation Based Nonlocal Means (NLM-SVB) method shows improved PSNR and SSIM over nonlocal means and other recent variations when retrieving a denoised image.
Automatic Brain Tumour Detection Using Symmetry Information
Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
Optimized Neural Network for Classification of Multispectral Images
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
Compressed sensing techniques for sensor data using unsupervised learning
This presentation gives a basic introduction to compressed sensing techniques and their applications to medical imaging, sensor network, wearable sensors and recommendation systems with my personal interpretations and opinions.
Quality Compression for Medical Big Data X-Ray Image using Biorthogonal 5.5 W...
Medical Big Data (MBD) consists of very useful type of information. It is very important for a physician for decision making and treatments to cure the patient. For accurate diagnosis, data availability is the most important factor. MBD over network needs intelligent compression schemes so that it is transferred to the destination by utilizing available bandwidth. Biorthogonal 5.5 Wavelet Compression scheme compress the MBD without losing the important information, thus making the information reliable and less in size; transference by efficient bandwidth utilization from source to destination.
Labeling fundus images for classification models
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
Development of algorithm for identification of maligant growth in cancer usin...
The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The various algorithms had been proposed earlier by many researchers within the past, but the accuracy of prediction is usually a challenging task. During this work, a man-made neural networ based methodology is proposed to seek out the irregular growth of lung tissues. Higher probability of detection is taken as a goal to urge an automatic tool, with great accuracy. The best feature sets derived from Haralick Gray level co occurrence Matrix and used because the dimension reduction way for feeding neural network. During this work, a binary Binary classifier neural network has been proposed to spot the traditional images out of all the images. The potential of the proposed neural network has been quantitatively computed using confusion matrix and located in terms of accuracy.
Dense Matrix Multiplication in Java
The document discusses decomposing matrix multiplication into smaller tasks by partitioning input and output matrices. It uses dynamic mapping with Java's ExecutorService to distribute the tasks across threads. The matrices are multiplied by decomposing them into 36 subtasks based on a 6x6 partitioning, and ExecutorService provides methods for managing asynchronous task execution and tracking progress.
Parallel architecture &programming
This document discusses parallel architecture and parallel programming. It begins with an introduction to von Neumann architecture and serial computation. Then it defines parallel architecture, outlines its benefits, and describes classifications of parallel processors including multiprocessor architectures. It also discusses parallel programming models, how to design parallel programs, and examples of parallel algorithms. Specific topics covered include shared memory and distributed memory architectures, message passing and data parallel programming models, domain and functional decomposition techniques, and a case study on developing parallel web applications using Java threads and mobile agents.
More Related Content
What's hot
Survey of using gpu cuda programming model
With the technology development of medical industry, processing data is expanding rapidly and computation time also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse sequences and the growing complexity of algorithms. Graphics processing unit (GPU) addresses these problems and gives the solutions for using their features such as, high computation throughput, high memory bandwidth, support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA) is a popular GPU programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed and the computational gain is discussed. A few open issues, hardware configurations and optimization principles of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.
Ieee brain tumor
This document presents a comparative study of two segmentation methods - k-means clustering and fuzzy c-means clustering with genetic algorithm - for detecting brain tumors in MRI images. K-means clustering is used to segment MRI images into clusters and identify tumor regions. Fuzzy c-means clustering with genetic algorithm aims to improve upon k-means by eliminating over-segmentation issues and providing faster, more efficient clustering results. The experimental results indicate fuzzy c-means performs better than k-means for brain tumor segmentation. The document also reviews several other related works applying techniques like edge detection and probabilistic neural networks to segment brain tumors from MRI scans.
Clustering of medline documents using semi supervised spectral clustering
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
Deep Learning-based Fully Automated Detection and Quantification of Acute Inf...
Presented work is accepted at RSNA 2020, Scientific Section.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
An annotation sparsification strategy for 3D medical image segmentation via r...
Review : An annotation sparsification strategy for 3D medical image segmentation via representative selection and self-training (University of Notre Dame , AAAI 2020)
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
H0114857
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING
Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edge detector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used for subsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combine the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accurate background is identified.
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from XailientEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.Mri brain image segmentatin and classification by modified fcm &svm akorithm
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A ...
The document discusses a proposed unified framework for progressively recovering images from corrupted observations through hybrid graph Laplacian regularization. The framework first constructs a multiscale representation of the target image using a Laplacian pyramid. It then progressively recovers the degraded image from coarse to fine scales, allowing for recovery of sharp edges and texture. Within each scale, a graph Laplacian regularization model is learned that minimizes error while preserving image structure. Between scales, the model is extended to a high-dimensional feature space to describe interscale correlations and propagate local structure regularity from coarser to finer scales. Experimental results on impulse noise removal demonstrate better performance than state-of-the-art algorithms.
Similarity validation based nonlocal means image denoising
This document discusses a method to improve the performance of nonlocal means image denoising. It proposes a pre-processing step that eliminates dissimilar patches from the search neighborhood before denoising. A hard threshold is calculated based on the distribution of distances between noisy similar patches. The proposed Similarity Validation Based Nonlocal Means (NLM-SVB) method shows improved PSNR and SSIM over nonlocal means and other recent variations when retrieving a denoised image.
Automatic Brain Tumour Detection Using Symmetry Information
Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
Optimized Neural Network for Classification of Multispectral Images
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
Compressed sensing techniques for sensor data using unsupervised learning
This presentation gives a basic introduction to compressed sensing techniques and their applications to medical imaging, sensor network, wearable sensors and recommendation systems with my personal interpretations and opinions.
Quality Compression for Medical Big Data X-Ray Image using Biorthogonal 5.5 W...
Medical Big Data (MBD) consists of very useful type of information. It is very important for a physician for decision making and treatments to cure the patient. For accurate diagnosis, data availability is the most important factor. MBD over network needs intelligent compression schemes so that it is transferred to the destination by utilizing available bandwidth. Biorthogonal 5.5 Wavelet Compression scheme compress the MBD without losing the important information, thus making the information reliable and less in size; transference by efficient bandwidth utilization from source to destination.
Labeling fundus images for classification models
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
Development of algorithm for identification of maligant growth in cancer usin...
The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The various algorithms had been proposed earlier by many researchers within the past, but the accuracy of prediction is usually a challenging task. During this work, a man-made neural networ based methodology is proposed to seek out the irregular growth of lung tissues. Higher probability of detection is taken as a goal to urge an automatic tool, with great accuracy. The best feature sets derived from Haralick Gray level co occurrence Matrix and used because the dimension reduction way for feeding neural network. During this work, a binary Binary classifier neural network has been proposed to spot the traditional images out of all the images. The potential of the proposed neural network has been quantitatively computed using confusion matrix and located in terms of accuracy.
What's hot (20)
Clustering of medline documents using semi supervised spectral clustering
Clustering of medline documents using semi supervised spectral clustering
Deep Learning-based Fully Automated Detection and Quantification of Acute Inf...
Deep Learning-based Fully Automated Detection and Quantification of Acute Inf...
An annotation sparsification strategy for 3D medical image segmentation via r...
An annotation sparsification strategy for 3D medical image segmentation via r...
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHM
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
Mri brain image segmentatin and classification by modified fcm &svm akorithm
Mri brain image segmentatin and classification by modified fcm &svm akorithm
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...
Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A ...
Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A ...
Similarity validation based nonlocal means image denoising
Similarity validation based nonlocal means image denoising
Automatic Brain Tumour Detection Using Symmetry Information
Automatic Brain Tumour Detection Using Symmetry Information
Optimized Neural Network for Classification of Multispectral Images
Optimized Neural Network for Classification of Multispectral Images
Compressed sensing techniques for sensor data using unsupervised learning
Compressed sensing techniques for sensor data using unsupervised learning
Quality Compression for Medical Big Data X-Ray Image using Biorthogonal 5.5 W...
Quality Compression for Medical Big Data X-Ray Image using Biorthogonal 5.5 W...
Development of algorithm for identification of maligant growth in cancer usin...
Development of algorithm for identification of maligant growth in cancer usin...
Viewers also liked
Dense Matrix Multiplication in Java
The document discusses decomposing matrix multiplication into smaller tasks by partitioning input and output matrices. It uses dynamic mapping with Java's ExecutorService to distribute the tasks across threads. The matrices are multiplied by decomposing them into 36 subtasks based on a 6x6 partitioning, and ExecutorService provides methods for managing asynchronous task execution and tracking progress.
Parallel architecture &programming
This document discusses parallel architecture and parallel programming. It begins with an introduction to von Neumann architecture and serial computation. Then it defines parallel architecture, outlines its benefits, and describes classifications of parallel processors including multiprocessor architectures. It also discusses parallel programming models, how to design parallel programs, and examples of parallel algorithms. Specific topics covered include shared memory and distributed memory architectures, message passing and data parallel programming models, domain and functional decomposition techniques, and a case study on developing parallel web applications using Java threads and mobile agents.
Parallel Computing
This document discusses parallel computing. It begins by defining parallel processing as using simultaneous data processing tasks to save time and/or money and solve larger problems. It then discusses how parallel computing uses multiple compute resources simultaneously to solve computational problems. Some examples of parallel phenomena in nature and technology are provided. The document outlines several areas where parallel computing is applied, including physics, bioscience, and computer science. It discusses the benefits of parallel computing in saving time and money and solving larger problems too large for a single computer. Finally, it briefly mentions ways to classify parallel computers and some basic requirements for achieving parallel execution.
Parallel computing
Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem faster. It allows for larger problems to be solved and provides cost savings over serial computing. There are different models of parallelism including data parallelism and task parallelism. Flynn's taxonomy categorizes computer architectures as SISD, SIMD, MISD and MIMD based on how instructions and data are handled. Shared memory and distributed memory are two common architectures that differ in scalability and communication handling. Programming models include shared memory, message passing and data parallel approaches. Design considerations for parallel programs include partitioning work, communication between processes, and synchronization.
Extreme programming (xp) | David Tzemach
It’s simply the best presentation that explains the agile methodology of Extreme Programming!
Overview
1. What is Extreme programming?
2. Extreme programming as an agile methodology.
3. The values of Extreme programming
4. The Activities of Extreme programming
5. The 12 core practices of Extreme programming
6. The roles of Extreme programming
Enjoy :)
Parallel Computing
Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
Visit www.ameyawaghmare.wordpress.com for more info
Applications of paralleL processing
This document discusses various applications of parallel processing. It describes how parallel processing is used in numeric weather prediction to forecast weather by processing large amounts of observational data. It is also used in oceanography and astrophysics to study oceans and conduct particle simulations. Other applications mentioned include socioeconomic modeling, finite element analysis, artificial intelligence, seismic exploration, genetic engineering, weapon research, medical imaging, remote sensing, energy exploration, and more. The document also discusses loosely coupled and tightly coupled multiprocessors and the differences between the two approaches.
Parallel processing Concepts
This document discusses parallel processing and parallel organizations. It describes four types of parallel organizations: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). MIMD systems are further broken down into shared memory and distributed memory architectures. Cache coherence protocols like MESI are discussed for maintaining consistency across caches in shared memory multiprocessors.
Introduction to parallel processing
This presentation contains flynn's classification,difference between SIMD and MIMD some applications of parallel processing
Mobile computing
This document discusses mobile computing and computer networks. It compares wired and mobile networks, describing differences in bandwidth, resources, and connectivity. It then outlines various types of wireless networks and devices like laptops, PDAs, and cell phones. Examples of mobile computing applications are given for fields like real estate, law, and transportation. Challenges of mobile computing are addressed as well as the future potential of increased processing power and integrated circuits to improve the technology. References are provided on related research papers and websites exploring topics in mobile object databases, mobile computing issues and solutions.
Extreme programming (xp)
This document provides an overview of Extreme Programming (XP), an agile software development methodology. It discusses XP's history and features, which include short 2-week development cycles, pair programming, test-driven development, and frequent refactoring. The core principles of XP are also examined, such as incremental planning, small releases, simple design, and sustainable pace. Various phases of the XP process are outlined, from exploration to productionizing. Requirements are captured as scenarios and prioritized by the customer. Automated testing is a key practice in XP. Both advantages like collective code ownership and disadvantages like its unsuitability for large projects are noted.
Parallel computing
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
Parallel Algorithms Advantages and Disadvantages
Parallel algorithms can increase throughput by using multiple processing units to perform independent tasks simultaneously. However, parallelization also introduces limitations. Amdahl's law dictates that speedup from parallelization is limited by the fraction of the algorithm that must execute sequentially. Complexity in designing, implementing, and maintaining parallel programs can outweigh performance benefits for some problems. Other challenges include data dependencies, portability across systems, scalability to larger problem and system sizes, and potential for parallel slowdown rather than speedup.
Introduction To Mobile Computing
The document provides an introduction to mobile computing. It discusses how mobile computing allows transmission of data without a fixed physical link. It describes the growth of mobile voice communication and how mobile data communication has become important. It discusses existing cellular network architecture and technologies like GSM. It covers advances in technology that have enabled mobile computing. It describes the different types of device mobility and portability. It provides examples of mobile computing applications in vehicles, emergencies, business, and for information services. It also discusses the history and market for mobile communications and some open research topics in the field.
Eye gaze communication
The document discusses the Eye Gaze system, which allows people with physical disabilities to control devices with their eyes. It describes how the system works by tracking a user's eye movements to select on-screen options. The document outlines who can benefit from the system, its various components and menus, applications, and future advancements like improved portability and tracking for limited eye control. It concludes that eye tracking interfaces can aid application control if used sensibly given the voluntary and involuntary nature of eye movements.
Mobile Computing
hey!!!!! everybody dats was simple ppt on mobile computing as u all aware dat d world is not stationary things are getting change technology is rocking all over so lets get into in it
and plz. dont forget to comment on my work weather u lik or not
E Learning Presentation
The document discusses eLearning in higher education and training. It provides examples of different forms of eLearning like learning management systems, Moodle, browser-based training, Open University, and distance learning. It also discusses the positives of eLearning in increasing access and student engagement, and the challenges of eLearning like student and instructor skills, technical issues, and managing student motivation in distance learning.
Viewers also liked (17)
Similar to Parallel Computing Application
Dp2 ppt by_bikramjit_chowdhury_final
Optimize Convolutional Neural Network by optimizing algorithm and improving with parallelization with OpenMP and OpenMPI
Dilated Inception U-Net for Nuclei Segmentation in Multi-Organ Histology Images
The document summarizes a study that used a Dilated Inception U-Net model for nuclei segmentation in histology images. Key points:
1. A Dilated Inception U-Net model was used to segment nuclei in histology images, which employs dilated convolutions to efficiently generate feature maps over a large input area.
2. The model was tested on the MoNuSeg dataset containing H&E stained images. Preprocessing included color normalization, data augmentation, and extracting 256x256 patches.
3. The Dilated Inception U-Net modifies the classic U-Net by replacing convolutional blocks with dilated inception blocks containing 1x1 and 3x3 filters with different dilation rates, allowing it to
GRID COMPUTING
The document discusses grid computing in remote sensing data processing. It describes how grid computing can help process huge amounts of remote sensing data in real-time by distributing processing across networked computers. Key requirements for a grid environment for remote sensing include sharing computational and software resources, managing resources, and supporting different data formats. Case studies demonstrate how grid middleware can improve the efficiency of tasks like image deblurring and generating lookup tables for aerosol analysis.
IMAGE SEGMENTATION AND ITS TECHNIQUES
This document discusses various techniques for image segmentation. It begins with an abstract discussing image segmentation and its importance in image processing. It then discusses different types of image segmentation like semantic and instance segmentation.
The document then discusses implementation of different image segmentation techniques. It implements region-based segmentation using Mask R-CNN. It performs thresholding-based segmentation using simple thresholding, Otsu's automatic thresholding. It also implements clustering-based segmentation using K-means and Fuzzy C-means. Furthermore, it implements edge-based segmentation using gradient-based techniques like Sobel and Prewitt, and Gaussian-based techniques like Laplacian and Canny edge detectors. Code snippets and output images are provided.
A Survey on Image Processing using CNN in Deep Learning
This document discusses the use of convolutional neural networks (CNNs) for image processing tasks. It provides an overview of CNNs and their application in image classification. The document then reviews several papers that have applied CNNs to tasks like image classification, object detection, and image segmentation. Some key advantages of CNNs discussed are their ability to directly take images as input without needing separate preprocessing steps. However, challenges include overfitting when training data is limited and complex images can confuse networks. The document concludes that CNN performance improves with more network layers and training data. CNNs are widely used for computer vision tasks due to their strong image feature extraction capabilities.
End-to-end deep auto-encoder for segmenting a moving object with limited tra...
The document proposes two end-to-end deep auto-encoder approaches for segmenting moving objects from surveillance videos when limited training data is available. The first approach uses transfer learning with a pre-trained VGG-16 model as the encoder and its transposed architecture as the decoder. The second approach uses a multi-depth auto-encoder with convolutional and upsampling layers. Both approaches apply data augmentation techniques like PCA and traditional methods to increase the training data size. The models are trained and evaluated on the CDnet2014 dataset, achieving better performance than other models trained with limited data.
Image super resolution using Generative Adversarial Network.
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
imagefiltervhdl.pptx
This document provides an overview of a project that implemented image filtering using VHDL on an FPGA board. It discusses designing filters like average, Sobel, Gaussian, and Laplacian filters. Cache memory and a processing unit were developed to hold pixel values and apply filter kernels. Different methods for multiplication in the convolution process were evaluated. Results showed the output images after applying each filter both in software and on the FPGA board. In conclusion, FPGAs provide reconfigurable, accelerated processing for image applications like filtering compared to general purpose computers.
2017 09-ohkawa-MCSoC2017-presen
This document proposes a design method for efficient parallel processing in 3D standard-chip stacking systems using a standard bus. It presents a model for mapping parallel algorithms to a 3D-SCSS and describes a design flow. As an example, it maps the scale pyramid generation process of an image recognition algorithm across multiple processor chips in the 3D-SCSS. Analysis shows the independent resize approach reduces data transfer compared to iterative resize, though it requires synchronization. Estimated power consumption is a minimum of 691.2μW for data transfer at 10 frames per second.
REVIEW ON OBJECT DETECTION WITH CNN
This document reviews object detection techniques using convolutional neural networks (CNNs). It begins with introducing object detection and CNNs. It then discusses the problem of object detection in computer vision and the need for more precise and accurate detection systems. The majority of the document reviews eight previous works that developed algorithms to improve object detection systems, including R-CNN and approaches using K-SVD, deep equilibrium models, non-local networks, transformers, and selective kernel networks. It evaluates these approaches and their abilities to achieve high detection rates while requiring fewer computations or model parameters. The document provides an overview of recent research aiming to advance CNN-based object detection.
GSU-RF-2013-Reddy-4
This document summarizes a research project on applying nanotechnology to cognitive radio and electronic warfare. It outlines the project members and faculty leads, problem statement, approach, and schedule. The problem aims to identify how nanotechnology can address spectrum sensing requirements for cognitive radio. The approach includes a literature review on integrating nanotechnology into wireless networks and studying the role of GPU technology in nanocomputing. Students conducted this research and a workshop on robotics and cybersecurity for high school students.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
IRJET- 3D Object Recognition of Car Image Detection
This document summarizes research on 3D object recognition of car images using depth data from a Kinect sensor. The researchers used point cloud analysis techniques including VFH, CRH descriptors and ICP algorithms to match objects in 3D space. The approach involved preprocessing the point cloud to isolate individual objects, extracting descriptors, matching objects to models in a database, and verifying matches. Preliminary results showed the approach could successfully recognize objects like soda cans but performance was best at distances under 1 meter from the sensor. The goal is to enable applications like gesture controls and height estimation using 3D object detection.
Performance of Weighted Least Square Filter Based Pan Sharpening using Fuzzy ...
This document proposes a new algorithm for pan sharpening images that combines weighted least squares filtering with fuzzy logic. It summarizes previous research on image fusion techniques like principal component analysis and discrete cosine transformation. The proposed algorithm applies fuzzy logic to evaluate membership functions and attain a pan sharpened image. It then uses a weighted least squares filter for pan sharpening. The algorithm is implemented in MATLAB and evaluated based on metrics like root mean square error, peak signal-to-noise ratio, and mean square error. Results show the proposed technique improves upon existing methods by reducing errors and increasing quality measurements of the fused images.
BRAINREGION.pptx
wqewewqeqweqweqwewqewqewqewqewqewqewewewewqewqewqewqewewqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwqwq
IRJET- A Survey on Medical Image Interpretation for Predicting Pneumonia
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
Table of Contents
This project aims to implement a recurrent neural network module for the ACL Neural Toolkit to expand its capabilities. A team will design the neuron and network models, develop a training algorithm, and integrate it into the Toolkit over 5 months. Milestones include preliminary models and algorithms, and incorporating it into the Toolkit to offer a necessary addition that can solve more complex problems than other methods.
Microscopy images segmentation algorithm based on shearlet neural network
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
Volume 2-issue-6-2200-2204
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
Volume 2-issue-6-2200-2204
This document summarizes research on using graph partitioning techniques to solve digital circuit layout problems. It discusses how the digital circuit layout problem is a constrained optimization problem that is NP-hard. It then reviews previous work on using techniques like min-cut bipartitioning, multi-way partitioning algorithms, and spectral graph partitioning to solve the problem. The document concludes by analyzing evolutionary approaches that have been used, including genetic algorithms, memetic algorithms, ant colony optimization, and particle swarm intelligence. It finds that these approaches are dependent on representation and initialization but can produce quality solutions for small circuits.
Similar to Parallel Computing Application (20)
Dilated Inception U-Net for Nuclei Segmentation in Multi-Organ Histology Images
Dilated Inception U-Net for Nuclei Segmentation in Multi-Organ Histology Images
A Survey on Image Processing using CNN in Deep Learning
A Survey on Image Processing using CNN in Deep Learning
End-to-end deep auto-encoder for segmenting a moving object with limited tra...
End-to-end deep auto-encoder for segmenting a moving object with limited tra...
Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...
IRJET- 3D Object Recognition of Car Image Detection
IRJET- 3D Object Recognition of Car Image Detection
Performance of Weighted Least Square Filter Based Pan Sharpening using Fuzzy ...
Performance of Weighted Least Square Filter Based Pan Sharpening using Fuzzy ...
IRJET- A Survey on Medical Image Interpretation for Predicting Pneumonia
IRJET- A Survey on Medical Image Interpretation for Predicting Pneumonia
Microscopy images segmentation algorithm based on shearlet neural network
Microscopy images segmentation algorithm based on shearlet neural network
Recently uploaded
skeleton System.pdf (skeleton system wow)
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف أحرف الخلاف للقراء العشرةأعد أحرف الخلاف بالتلوين وصلا سمير بسيوني غفر الله له
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
Bossa N’ Roll Records by Ismael Vazquez.
Bossa N Roll Records presentation by Izzy Vazquez for Music Retail and Distribution class at Full Sail University
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
A Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as discipline
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Stack Memory Organization of 8086 Microprocessor
The stack memory organization of 8086 microprocessor.
Recently uploaded (20)
Juneteenth Freedom Day 2024 David Douglas School District
Juneteenth Freedom Day 2024 David Douglas School District
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
Parallel Computing Application
- 1. Research Article: CUDA-Accelerated Geodesic Ray- Tracing for Fiber Tracking by E.van Aart et al. Group Member: Nik Fatinah Binti N. Mohd Farid (2012790807) Hanis Salwan Binti Mobidin (2012144449)
- 2. Area of Domain: Medical Imaging Trend: • This area is expected to continue growing as more sophisticated and challenging medical imaging, image processing, and high-order data visualization problems are being addressed. • The ongoing cost drop in computational tools and their wide accessibility play a center role. • There are various imaging modalities that apply the parallel computation. Master of Comp Science CS707 (CSC780) 2
- 3. Introduction • This paper imparts an accelerated algorithm for tracking fiber in brain. • Noninvasive diffusion weighted imaging (MRI technique) followed by reconstructing the brain fiber structure provides a distinctive way to examine the complex structures inside the brain in microscopic level. • The process of using the measured diffusion to reconstruct the underlying fiber structure is called fiber tracking. • There are many different fiber tracking algorithms have been developed such as Diffusion Tensor Imaging (DTI), Fiber Tracking Algorithms and Geodesic Algorithm. Master of Comp Science CS707 (CSC780) 3
- 4. Introduction Continue.. • This paper concentrates on an approach in which fibers are constructed by finding geodesics on a Riemannian manifold which called as geodesic ray-tracing technique. • Geodesics-shortest path on a Riemannian manifold. • Riemannian manifold-surface. • Advantages of using Geodesic ray-tracing technique are it is more robust to noise and capable to recognize multi-valued solutions (make this technique appropriate to analyze complex structures). Master of Comp Science CS707 (CSC780) 4
- 5. Focused Problem • Although geodesic ray-tracing technique poses several advantages yet it has some disadvantages such as: It is computationally expensive . Only find a single possible connection between target regions ( often not right solution in complex areas). • To reliably find all geodesics between the seed point and target region, huge amount of fibers are needed. Master of Comp Science CS707 (CSC780) 5
- 6. Proposed Solution • To overcome this problem, the authors decide to parallelize the geodesic ray-tracing algorithm. • We accelerated the algorithm by implementing a highly parallel version on a GPU, using NVIDIA’s CUDA platform . • CUDA-is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). Master of Comp Science CS707 (CSC780) 6
- 7. The Structure of CUDA-enabled GPU Master of Comp Science CS707 (CSC780) 7
- 8. Significant of using Parallel Programming • There is a significant reduction in running time by a factor of up to 40 compared to a multithreaded CPU implementation. • Can greatly increases the applicability of the algorithm. Master of Comp Science CS707 (CSC780) 8
- 9. References • https://developer.nvidia.com/about-cuda • http://downloads.hindawi.com/journals/ijbi/20 11/840181.pdf • http://www.hindawi.com/journals/ijbi/2011/69 8908/ Master of Comp Science CS707 (CSC780) 9
- 10. Master of Comp Science CS707 (CSC780) 10